Cancer subtype identification aims to partition the cancer patients into different subgroups with distinct clinical phenotypes, which is important for accurate diagnosis and treatment planning. The recent surge in multi-omics data has spurred research into integrative subtype identification, and multi-view clustering is widely used for identifying the underlying potential subtypes in an unsupervised manner. However, most existing approaches only consider the single-level relations within each view and cannot fully explore the high-order relations across views. In this paper, we propose a new Multi-Relational Multi-View Clustering (MRMVC) method to address these issues, which treats multi-omics data as different views, and thoroughly explores multi-level intra-view and inter-view relations for subtype identification. It fully learns the pairwise similarity between samples based on (1) intra-view global relations that encourage the intra-class cohesion, (2) intra-view local relations that promote the inter-class separability, and (3) inter-view high-order relations that align the multiple graphs, enabling the discovery of intrinsic similarities and enhancing the clustering performance. Experiments on generic datasets and multi-omics cancer datasets illustrate the efficacy and superiority of the proposed method in clustering and identifying more distinct cancer subtypes.

中文翻译：

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多关系多视图聚类及其在癌症亚型识别中的应用


癌症亚型识别旨在将癌症患者划分为具有不同临床表型的不同亚组，这对于准确的诊断和治疗计划非常重要。最近多组学数据的激增刺激了对综合亚型识别的研究，多视图聚类被广泛用于以无监督方式识别潜在的潜在亚型。然而，大多数现有的方法只考虑每个视图中的单级关系，而无法完全探索跨视图的高阶关系。在本文中，我们提出了一种新的多关系多视图聚类 （MRMVC） 方法来解决这些问题，该方法将多组学数据视为不同的视图，并深入探讨了用于亚型识别的多层次视图内和视图间关系。它根据 （1） 促进类内聚的视图内全局关系，（2） 促进类间可分性的视图内局部关系，以及 （3） 对齐多个图的视图间高阶关系，能够发现内在相似性并提高聚类性能，从而完全学习样本之间的成对相似性。在通用数据集和多组学癌症数据集上的实验说明了所提出的方法在聚类和识别更不同的癌症亚型方面的有效性和优越性。